Development and Implementation of a Digital Quality Measure of Emergency Cancer Diagnosis

Author:

Kapadia Paarth12,Zimolzak Andrew J.12ORCID,Upadhyay Divvy K.3ORCID,Korukonda Saritha3,Murugaesh Rekha Riyaa3,Mushtaq Umair12,Mir Usman12ORCID,Murphy Daniel R.12ORCID,Offner Alexis12ORCID,Abel Gary A.4ORCID,Lyratzopoulos Georgios5ORCID,Mounce Luke T.A.4ORCID,Singh Hardeep12ORCID

Affiliation:

1. Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, TX

2. Department of Medicine, Baylor College of Medicine, Houston, TX

3. Geisinger, Danville, PA

4. University of Exeter, Exeter, United Kingdom

5. Epidemiology of Cancer Healthcare and Outcomes, Institute of Epidemiology and Health Care, University College London, London, United Kingdom

Abstract

PURPOSE Missed and delayed cancer diagnoses are common, harmful, and often preventable. Automated measures of quality of cancer diagnosis are lacking but could identify gaps and guide interventions. We developed and implemented a digital quality measure (dQM) of cancer emergency presentation (EP) using electronic health record databases of two health systems and characterized the measure's association with missed opportunities for diagnosis (MODs) and mortality. METHODS On the basis of literature and expert input, we defined EP as a new cancer diagnosis within 30 days after emergency department or inpatient visit. We identified EPs for lung cancer and colorectal cancer (CRC) in the Department of Veterans Affairs (VA) and Geisinger from 2016 to 2020. We validated measure accuracy and identified preceding MODs through standardized chart review of 100 records per cancer per health system. Using VA's longitudinal encounter and mortality data, we applied logistic regression to assess EP's association with 1-year mortality, adjusting for cancer stage and demographics. RESULTS Among 38,565 and 2,914 patients with lung cancer and 14,674 and 1,649 patients with CRCs at VA and Geisinger, respectively, our dQM identified EPs in 20.9% and 9.4% of lung cancers, and 22.4% and 7.5% of CRCs. Chart reviews revealed high positive predictive values for EPs across sites and cancer types (72%-90%), and a substantial percent represented MODs (48.8%-84.9%). EP was associated with significantly higher odds of 1-year mortality for lung cancer and CRC (adjusted odds ratio, 1.78 and 1.83, respectively, 95% CI, 1.63 to 1.86 and 1.61 to 2.07). CONCLUSION A dQM for cancer EP was strongly associated with both mortality and MODs. The findings suggest a promising automated approach to measuring quality of cancer diagnosis in US health systems.

Publisher

American Society of Clinical Oncology (ASCO)

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